docs: def precision_recall_curve(y_true, scores, distances=False):
      """Precision-recall curve
  
      Parameters
      ----------
      y_true : (n_samples, ) array-like
          Boolean reference.
      scores : (n_samples, ) array-like
          Predicted score.
      distances : boolean, optional
          When True, indicate that `scores` are actually `distances`
  
      Returns
      -------
      precision : numpy array
          Precision
      recall : numpy array
          Recall
      thresholds : numpy array
          Corresponding thresholds
      auc : float
          Area under curve
  
      """
  
      if distances:
          scores = -scores
  
      precision, recall, thresholds = sklearn.metrics.precision_recall_curve(
          y_true, scores, pos_label=True)
  
      if distances:
          thresholds = -thresholds
  
      auc = sklearn.metrics.auc(precision, recall, reorder=True)
  
      return precision, recall, thresholds, auc